Depth maps play a crucial role in various practical applications such as computer vision,augmented reality,and autonomous driving.How to obtain clear and accurate depth information in video depth estimation is a signi...Depth maps play a crucial role in various practical applications such as computer vision,augmented reality,and autonomous driving.How to obtain clear and accurate depth information in video depth estimation is a significant challenge faced in the field of computer vision.However,existing monocular video depth estimation models tend to produce blurred or inaccurate depth information in regions with object edges and low texture.To address this issue,we propose a monocular depth estimation model architecture guided by semantic segmentation masks,which introduces semantic information into the model to correct the ambiguous depth regions.We have evaluated the proposed method,and experimental results show that our method improves the accuracy of edge depth,demonstrating the effectiveness of our approach.展开更多
Self-supervised monocular depth estimation has emerged as a major research focus in recent years,primarily due to the elimination of ground-truth depth dependence.However,the prevailing architectures in this domain su...Self-supervised monocular depth estimation has emerged as a major research focus in recent years,primarily due to the elimination of ground-truth depth dependence.However,the prevailing architectures in this domain suffer from inherent limitations:existing pose network branches infer camera ego-motion exclusively under static-scene and Lambertian-surface assumptions.These assumptions are often violated in real-world scenarios due to dynamic objects,non-Lambertian reflectance,and unstructured background elements,leading to pervasive artifacts such as depth discontinuities(“holes”),structural collapse,and ambiguous reconstruction.To address these challenges,we propose a novel framework that integrates scene dynamic pose estimation into the conventional self-supervised depth network,enhancing its ability to model complex scene dynamics.Our contributions are threefold:(1)a pixel-wise dynamic pose estimation module that jointly resolves the pose transformations of moving objects and localized scene perturbations;(2)a physically-informed loss function that couples dynamic pose and depth predictions,designed to mitigate depth errors arising from high-speed distant objects and geometrically inconsistent motion profiles;(3)an efficient SE(3)transformation parameterization that streamlines network complexity and temporal pre-processing.Extensive experiments on the KITTI and NYU-V2 benchmarks show that our framework achieves state-of-the-art performance in both quantitative metrics and qualitative visual fidelity,significantly improving the robustness and generalization of monocular depth estimation under dynamic conditions.展开更多
Due to the lack of accurate data and complex parameterization,the prediction of groundwater depth is a chal-lenge for numerical models.Machine learning can effectively solve this issue and has been proven useful in th...Due to the lack of accurate data and complex parameterization,the prediction of groundwater depth is a chal-lenge for numerical models.Machine learning can effectively solve this issue and has been proven useful in the prediction of groundwater depth in many areas.In this study,two new models are applied to the prediction of groundwater depth in the Ningxia area,China.The two models combine the improved dung beetle optimizer(DBO)algorithm with two deep learning models:The Multi-head Attention-Convolution Neural Network-Long Short Term Memory networks(MH-CNN-LSTM)and the Multi-head Attention-Convolution Neural Network-Gated Recurrent Unit(MH-CNN-GRU).The models with DBO show better prediction performance,with larger R(correlation coefficient),RPD(residual prediction deviation),and lower RMSE(root-mean-square error).Com-pared with the models with the original DBO,the R and RPD of models with the improved DBO increase by over 1.5%,and the RMSE decreases by over 1.8%,indicating better prediction results.In addition,compared with the multiple linear regression model,a traditional statistical model,deep learning models have better prediction performance.展开更多
Rare earth-doped inorganic compounds contribute mostly to the family of persistent luminescent materials due to the versatile energy levels of rare earth ions.One of the key research aims is to match the trap level st...Rare earth-doped inorganic compounds contribute mostly to the family of persistent luminescent materials due to the versatile energy levels of rare earth ions.One of the key research aims is to match the trap level stemming from the doped rare earth ion or intrinsic defects to the electronic structure of the host,and therefore thermoluminescence measurement becomes a radical technology in studying trap depth,which is one of the significant parameters that determine the properties of persistent luminescence and photostimulated luminescence.However,the results of trap depth obtained by different thermoluminescence methods are quite different so that they are not comparable.Herein,we analyzed different thermoluminescence methods,selected and improved the traditional peak position method of T_(m)/500 to be E=(-0.94Inβ+30.09)kT_(m).Only the experimental heating rate(β)is needed additionally,but the accuracy is improved greatly in most cases.This convenient and accurate method will accelerate the discovery of novel rare earth-doped materials.展开更多
Spatial computing and augmented reality are advancing rapidly,with the goal of seamlessly blending virtual and physical worlds.However,traditional depth-sensing systems are bulky and energy-intensive,limiting their us...Spatial computing and augmented reality are advancing rapidly,with the goal of seamlessly blending virtual and physical worlds.However,traditional depth-sensing systems are bulky and energy-intensive,limiting their use in wearable devices.To overcome this,recent research by X.Liu et al.presents a compact binocular metalens-based depth perception system that integrates efficient edge detection through an advanced neural network.This system enables accurate,realtime depth mapping even in complex environments,enhancing potential applications in augmented reality,robotics,and autonomous systems.展开更多
During upward horizontal stratified backfill mining,stable backfill is essential for cap and sill pillar recovery.Currently,the primary method for calculating the required strength of backfill is the generalized three...During upward horizontal stratified backfill mining,stable backfill is essential for cap and sill pillar recovery.Currently,the primary method for calculating the required strength of backfill is the generalized three-dimensional(3 D)vertical stress model,which ignores the effect of mine depth,failing to obtain the vertical stress at different positions along stope length.Therefore,this paper develops and validates an improved 3 D model solution through numerical simulation in Rhino-FLAC^(3D),and examines the stress state and stability of backfill under different conditions.The results show that the improved model can accurately calculate the vertical stress at different mine depths and positions along stope length.The error rates between the results of the improved model and numerical simulation are below 4%,indicating high reliability and applicability.The maximum vertical stress(σ_(zz,max))in backfill is positively correlated with the degree of rock-backfill closure,which is enhanced by mine depth and elastic modulus of backfill,while weakened by stope width and inclination,backfill friction angle,and elastic modulus of rock mass.Theσ_(zz,max)reaches its peak when the stope length is 150 m,whileσ_(zz,max)is insensitive to changes in rock-backfill interface parameters.In all cases,the backfill stability can be improved by reducingσ_(zz,max).The results provide theoretical guidance for the backfill strength design and the safe and efficient recovery of ore pillars in deep mining.展开更多
Coal seam water injection in tunnels is an effective technical measure for preventing coal mine rock bursts.This study used the improved split Hopkinson pressure bar(SHPB)to apply three equal static stresses to water-...Coal seam water injection in tunnels is an effective technical measure for preventing coal mine rock bursts.This study used the improved split Hopkinson pressure bar(SHPB)to apply three equal static stresses to water-saturated coal to simulate the initial stress environment of coal at different depths.Then,dynamic mechanical experiments were conducted on the saturated coal at different depths to investigate the effects of water saturation and depth on the coal samples’dynamic mechanical properties.Under uniaxial compression and without lateral compression,the strength of coal samples decreased to varying degrees in the saturated state;under different depth conditions,the dynamic strength of coal in the saturated state decreased compared with that in the natural state.However,compared with that at 0 m,the reduction in the strength of coal under the saturated condition at 200,400,600,and 800 m was significantly reduced.The findings of this study provide a basic theoretical foundation for the prevention and control of dynamic coal mine disasters.展开更多
Eye depth is an important agronomic trait affecting tubers'appearance,quality,and processing suitability.Hence,cultivating varieties with uniform shapes and shallow eye depth are important goals for potato breedin...Eye depth is an important agronomic trait affecting tubers'appearance,quality,and processing suitability.Hence,cultivating varieties with uniform shapes and shallow eye depth are important goals for potato breeding.In this study,based on the primary mapping of the tuber eyedepth locus using a small primary-segregating population,a large secondary-segregating population with 2100 individuals was used to map the eye-depth locus further.A major quantitative trait locus for eye-depth on chromosome 10 was identified(designated qEyd10.1)using BSAseq and traditional QTL mapping methods.The qEyd10.1 could explain 55.0%of the eye depth phenotypic variation and was further narrowed to a 309.10 kb interval using recombinant analysis.To predict candidate genes,tissue sectioning and RNA-seq of the specific tuber tissues were performed.Genes encoding members of the peroxidase superfamily with likely roles in indole acetic acid regulation were considered the most promising candidates.These results will facilitate marker-assisted selection for the shallow-eye trait in potato breeding and provide a solid basis for eye-depth gene cloning and the analysis of tuber eye-depth regulatory mechanisms.展开更多
AIM:To assess visual outcomes and satisfaction of a non-diffractive extended depth of focus(EDOF)intraocular lens(IOL)in individuals with ocular hypertension(OHT)and well-controlled mild glaucoma undergoing cataract s...AIM:To assess visual outcomes and satisfaction of a non-diffractive extended depth of focus(EDOF)intraocular lens(IOL)in individuals with ocular hypertension(OHT)and well-controlled mild glaucoma undergoing cataract surgery.METHODS:An investigator-initiated,single-center,prospective,interventional,noncomparative study conducted in Montreal,Canada.The study enrolled 31 patients(55 eyes)with OHT or mild glaucoma who received a non-diffractive EDOF IOL(Acrysof IQ Vivity).Participants underwent sequential cataract surgery with the Vivity IOL.Follow-up evaluations occurred at 1d,1,and 3mo postoperatively,assessing uncorrected distance,intermediate,and near visual acuity.Questionnaires(QUVID:Questionnaire for visual disturbances and IOLSAT:Intraocular lens satisfaction)were administered pre and post-operatively to measure visual disturbances and spectacle independence in various lighting.Safety parameters included intraocular pressure(IOP),glaucoma medications,spherical equivalence,mean deviation and pattern standard deviation or square root of lost variance on Octopus visual field.RESULTS:At 1 and 3mo postoperatively,significant improvements were observed in uncorrected distance and intermediate visual acuity.Spectacle independence was enhanced for distance and intermediate vision,especially in bright light settings.Spectacle-free intermediate vision was improved even in dim lighting.Visual disturbances,particularly glare symptoms,were reduced,and there was a notable decrease in IOP and glaucoma medication burden at 3mo.There was more hazy vision postoperatively with no impact on visual acuity and visual satisfaction.CONCLUSION:The non-diffractive EDOF lens improves distance and intermediate spectacle-free visual function in patients with OHT and well-controlled glaucoma.The findings highlight significant improvements in visual acuity,reduced glare,enhanced spectacle independence,and improved visual performance in different lighting conditions.展开更多
Maintaining the s-polarization state of laser beams is important to achieve high modulation depth in a laser-interference-based super-resolution structured illumination microscope(SR-SIM).However,the imperfect optical...Maintaining the s-polarization state of laser beams is important to achieve high modulation depth in a laser-interference-based super-resolution structured illumination microscope(SR-SIM).However,the imperfect optical components can depolarize the laser beams hence degenerating the modulation depth.Here,we first presented a direct measurement method designed to estimate the modulation depth more precisely by shifting illumination patterns with equal phase steps.This measurement method greatly reduces the dependence of modulation depths on the samples,and then developed a polarization optimization method to achieve high modulation depth at all orientations by actively and quantitatively compensating for the additional phase difference using a combination of waveplate and a liquid crystal variable retarder(LCVR).Experimental results demonstrate that our method can achieve illumination patterns with modulation depth higher than 0.94 at three orientations with only one LCVR voltage,which enables isotropic resolution improvement.展开更多
Object detection in occluded environments remains a core challenge in computer vision(CV),especially in domains such as autonomous driving and robotics.While Convolutional Neural Network(CNN)-based twodimensional(2D)a...Object detection in occluded environments remains a core challenge in computer vision(CV),especially in domains such as autonomous driving and robotics.While Convolutional Neural Network(CNN)-based twodimensional(2D)and three-dimensional(3D)object detection methods havemade significant progress,they often fall short under severe occlusion due to depth ambiguities in 2D imagery and the high cost and deployment limitations of 3D sensors such as Light Detection and Ranging(LiDAR).This paper presents a comparative review of recent 2D and 3D detection models,focusing on their occlusion-handling capabilities and the impact of sensor modalities such as stereo vision,Time-of-Flight(ToF)cameras,and LiDAR.In this context,we introduce FuDensityNet,our multimodal occlusion-aware detection framework that combines Red-Green-Blue(RGB)images and LiDAR data to enhance detection performance.As a forward-looking direction,we propose a monocular depth-estimation extension to FuDensityNet,aimed at replacing expensive 3D sensors with a more scalable CNN-based pipeline.Although this enhancement is not experimentally evaluated in this manuscript,we describe its conceptual design and potential for future implementation.展开更多
Workpiece rotational grinding is widely used in the ultra-precision machining of hard and brittle semiconductor materials,including single-crystal silicon,silicon carbide,and gallium arsenide.Surface roughness and sub...Workpiece rotational grinding is widely used in the ultra-precision machining of hard and brittle semiconductor materials,including single-crystal silicon,silicon carbide,and gallium arsenide.Surface roughness and subsurface damage depth(SDD)are crucial indicators for evaluating the surface quality of these materials after grinding.Existing prediction models lack general applicability and do not accurately account for the complex material behavior under grinding conditions.This paper introduces novel models for predicting both surface roughness and SDD in hard and brittle semiconductor materials.The surface roughness model uniquely incorporates the material’s elastic recovery properties,revealing the significant impact of these properties on prediction accuracy.The SDD model is distinguished by its analysis of the interactions between abrasive grits and the workpiece,as well as the mechanisms governing stress-induced damage evolution.The surface roughness model and SDD model both establish a stable relationship with the grit depth of cut(GDC).Additionally,we have developed an analytical relationship between the GDC and grinding process parameters.This,in turn,enables the establishment of an analytical framework for predicting surface roughness and SDD based on grinding process parameters,which cannot be achieved by previous models.The models were validated through systematic experiments on three different semiconductor materials,demonstrating excellent agreement with experimental data,with prediction errors of 6.3%for surface roughness and6.9%for SDD.Additionally,this study identifies variations in elastic recovery and material plasticity as critical factors influencing surface roughness and SDD across different materials.These findings significantly advance the accuracy of predictive models and broaden their applicability for grinding hard and brittle semiconductor materials.展开更多
In the dynamic scene of autonomous vehicles,the depth estimation of monocular cameras often faces the problem of inaccurate edge depth estimation.To solve this problem,we propose an unsupervised monocular depth estima...In the dynamic scene of autonomous vehicles,the depth estimation of monocular cameras often faces the problem of inaccurate edge depth estimation.To solve this problem,we propose an unsupervised monocular depth estimation model based on edge enhancement,which is specifically aimed at the depth perception challenge in dynamic scenes.The model consists of two core networks:a deep prediction network and a motion estimation network,both of which adopt an encoder-decoder architecture.The depth prediction network is based on the U-Net structure of ResNet18,which is responsible for generating the depth map of the scene.The motion estimation network is based on the U-Net structure of Flow-Net,focusing on the motion estimation of dynamic targets.In the decoding stage of the motion estimation network,we innovatively introduce an edge-enhanced decoder,which integrates a convolutional block attention module(CBAM)in the decoding process to enhance the recognition ability of the edge features of moving objects.In addition,we also designed a strip convolution module to improve the model’s capture efficiency of discrete moving targets.To further improve the performance of the model,we propose a novel edge regularization method based on the Laplace operator,which effectively accelerates the convergence process of themodel.Experimental results on the KITTI and Cityscapes datasets show that compared with the current advanced dynamic unsupervised monocular model,the proposed model has a significant improvement in depth estimation accuracy and convergence speed.Specifically,the rootmean square error(RMSE)is reduced by 4.8%compared with the DepthMotion algorithm,while the training convergence speed is increased by 36%,which shows the superior performance of the model in the depth estimation task in dynamic scenes.展开更多
Global change threatens mountainous plant communities,causing habitat displacement.Phylogenetic studies reveal evolutionary and ecological processes in community assembly.We examined taxonomic and phylogenetic diversi...Global change threatens mountainous plant communities,causing habitat displacement.Phylogenetic studies reveal evolutionary and ecological processes in community assembly.We examined taxonomic and phylogenetic diversity in Andean Páramos across altitudes.Our hypotheses were that increasing altitude is an environmental filter,as altitude is expected to be a stronger variable than soil depth.The Páramos,alpine vegetation in the Andes,range from 3,000 to 4,700 meters,with our plots spanning 3,200 to 4,100 meters.Sampling was conducted at six altitudinal levels,measuring soil depth,taxonomic,and phylogenetic diversity.Data analysis employed multiple linear regressions and mixed-effects models to assess the effects of soil depth and altitude.We sampled 110 angiosperm species from 70 genera,30 families,and 18 orders.Asterales and Poales were prominent.Species richness generally decreased with altitude but increased at the summit.Soil depth affected species richness and taxonomic diversity,while altitude did not.Phylogenetic diversity increased with soil depth and decreased with altitude.Phylogenetic turnover increased with altitude differences.The hypothesis that increasing altitude intensifies environmental filtering in the altitudeadapted Páramos resulting in lower species richness and more clustered phylogenetic structures,was rejected.Although species richness,Shannon diversity,and Simpson diversity decreased initially with increasing altitude,this trend was not linear because the summit presented intermediate species richness.The hypothesis that altitude is a stronger explanatory variable than soil depth was also rejected.Despite expectations,taxonomic results did not support altitude as an environmental filter,but soil depth.Greater altitude differences increased beta phylogenetic dissimilarity,supporting niche conservatism.展开更多
As shale gas technology has advanced,the horizontal well fracturing model has seen widespread use,leading to substantial improvements in industrial gas output from shale gas wells.Nevertheless,a swift decline in the p...As shale gas technology has advanced,the horizontal well fracturing model has seen widespread use,leading to substantial improvements in industrial gas output from shale gas wells.Nevertheless,a swift decline in the productivity of individual wells remains a challenge that must be addressed throughout the development process.In this study,gas wells with two different wellbore trajectory structures are considered,and the OLGA software is exploited to perform transient calculations on various tubing depth models.The results can be articulated as follows.In terms of flow patterns:for the deep well A1(upward-buckled),slug flow occurs in the Kick-off Point position and above;for the deep well B1(downward-inclined),slug flow only occurs in the horizontal section.Wells with downward-inclined horizontal sections are more prone to liquid accumulation issues.In terms of comparison to conventional wells,it is shown that deep shale gas wells have longer normal production durations and experience liquid accumulation later than conventional wells.With regard to optimal tubing placement:for well A1(upward-buckled),it is recommended to place tubing at the Kick-off Point position;for well B1(downward-inclined),it is recommended to place tubing at the lower heel of the horizontal section.Finally,in terms of production performance:well A1(upward-buckled)outperforms well B1(downward-inclined)in terms of production and fluid accumulation.In particular,the deep well A1 is 1.94 times more productive and 1.3 times longer to produce than conventional wells.Deep well B1 is 1.87 times more productive and 1.34 times longer than conventional wells.展开更多
Understanding water uptake depth and its relationship with functional traits offers valuable insights into resource-use partitioning among coexisting tree species as well as forest responses to drought.However,knowled...Understanding water uptake depth and its relationship with functional traits offers valuable insights into resource-use partitioning among coexisting tree species as well as forest responses to drought.However,knowledge about water uptake patterns in vertical soil layers,especially among increasingly widespread secondary forest tree species,remains limited.In this study,we investigated interspecific and seasonal variations in water uptake depth among seven coexisting tree species over a 2-year period in a warm-temperate secondary forest in central Japan.We also analyzed the relationships of water uptake depth with tree height and functional traits,including specific leaf area(SLA),leaf dry matter content(LDMC),leaf nitrogen(N)content,and wood density(WD),to discern resource-use and-acquisition strategies.Results revealed that taller trees,especially when soil water is scarce,tend to access deeper soil water sources,indicating that water source partitioning is correlated with tree height.This interspecific and temporal variation in water sources likely stratifies trees to facilitate coexistence within the forest.Water uptake depth was primarily associated with WD and LDMC:trees absorbing more water from shallow soils during dry conditions exhibited lower WD and LDMC,indicating a proactive resource-use strategy.Conversely,SLA and leaf N content were orthogonal to water uptake depth,suggesting that strategies for acquiring belowground and aboveground resources may differ.Considering the alternation of tree species composition during secondary forest succession,our study highlights the importance of further data collection regarding root water uptake depth along successional stages to understand dynamic shifts in water uptake sources.展开更多
Scour around bridge pier foundations is a complex phenomenon that can threaten structural stability.Accurate prediction of scour depth around compound piers remains challenging for bridge engineers.This study investig...Scour around bridge pier foundations is a complex phenomenon that can threaten structural stability.Accurate prediction of scour depth around compound piers remains challenging for bridge engineers.This study investigated the effect of foundation elevation on scour around compound piers and developed reliable scour depth prediction models for economical foundation design.Experiments were conducted under clear-water conditions using two circular piers:(1)a uniform pier(with a diameter of D)and(2)a compound pier consisting of a uniform pier resting on a circular foundation(with a foundation diameter(D_(f))of 2D)positioned at various elevations(Z)relative to the channel bed.Results showed that foundation elevation significantly affected scour depth.Foundations at or below the bed(Z/D≥0)reduced scour,while those projecting into the flow field(Z/D<0)increased scour.The optimal foundation elevation was found to be 0.1D below the bed level,yielding a 57%reduction in scour depth compared to the uniform pier due to its shielding effect against downflow and horseshoe vortices.In addition,regression,artificial neural network(ANN),and M5 model tree models were developed using experimental data from this and previous studies.The M5 model outperformed the traditional HEC-18 equation,regression,and ANN models,with a coefficient of determination greater than 0.85.Sensitivity analysis indicated that flow depth,foundation elevation,and diameter significantly influenced scour depth prediction,whereas sediment size had a lesser impact.展开更多
This paper analyzes the text of 3261 clauses of 20 RTAs signed by China,classifies them into 52 policy areas according to the international mainstream HMS method,and assigns them through coding.The clause depth of Ch...This paper analyzes the text of 3261 clauses of 20 RTAs signed by China,classifies them into 52 policy areas according to the international mainstream HMS method,and assigns them through coding.The clause depth of China’s RTAs is measured across three-dimensional systems(policy areas,clauses,and core clauses)and two generations of trade policy areas(WTO+,WTO-X,and all policy areas).It is observed that China’s RTAs exhibit greater depth in Industrial Products,Agricultural Products,TBT,Antidumping,Countervailing,and Investment,while showing comparatively less depth in Fiscal Policy,Innovation Policies,and related areas.展开更多
The Afyon-Akşehir and Sinanpaşa grabens,located in the eastern part of the Akşehir-Simav Fault System,are important sedimentary basins in the western Anatolia,Türkiye.This region,particularly the western of Afyon...The Afyon-Akşehir and Sinanpaşa grabens,located in the eastern part of the Akşehir-Simav Fault System,are important sedimentary basins in the western Anatolia,Türkiye.This region,particularly the western of Afyon-Akşehir Graben,is a significant region known for its geothermal potential.The study focuses on analyzing gravity data to identify structural elements and examine the geological structures in the basins.The edge detection and enhancement techniques such as total horizontal gradient,tilt angle of the total horizontal gradient,enhanced dip angle and curvature analysis were used to investigate the structural lineaments in the area.Furthermore,2D/3D gravity modeling techniques were utilized to investigate the sedimentary depths of the Afyon-Akşehir and Sinanpaşa grabens.Based on the findings from the edge detection studies,three distinct linear features were highlighted in addition to previously identified geological structures.3D gravity inversion modeling reveals sedimentary basin depths of up to 470 m in Sinanpaşa Graben and 720 m in the western Afyon-Akşehir Graben.As a result of the structural mapping and 2D/3D gravity modeling studies,a structural uplift that may be linked to geothermal activity was detected among the local depressions in the Afyon-Akşehir Graben.The obtained features may be of potential interest for geothermal exploration;therefore,further investigations using additional geophysical data are recommended.展开更多
文摘Depth maps play a crucial role in various practical applications such as computer vision,augmented reality,and autonomous driving.How to obtain clear and accurate depth information in video depth estimation is a significant challenge faced in the field of computer vision.However,existing monocular video depth estimation models tend to produce blurred or inaccurate depth information in regions with object edges and low texture.To address this issue,we propose a monocular depth estimation model architecture guided by semantic segmentation masks,which introduces semantic information into the model to correct the ambiguous depth regions.We have evaluated the proposed method,and experimental results show that our method improves the accuracy of edge depth,demonstrating the effectiveness of our approach.
基金supported in part by the National Natural Science Foundation of China under Grants 62071345。
文摘Self-supervised monocular depth estimation has emerged as a major research focus in recent years,primarily due to the elimination of ground-truth depth dependence.However,the prevailing architectures in this domain suffer from inherent limitations:existing pose network branches infer camera ego-motion exclusively under static-scene and Lambertian-surface assumptions.These assumptions are often violated in real-world scenarios due to dynamic objects,non-Lambertian reflectance,and unstructured background elements,leading to pervasive artifacts such as depth discontinuities(“holes”),structural collapse,and ambiguous reconstruction.To address these challenges,we propose a novel framework that integrates scene dynamic pose estimation into the conventional self-supervised depth network,enhancing its ability to model complex scene dynamics.Our contributions are threefold:(1)a pixel-wise dynamic pose estimation module that jointly resolves the pose transformations of moving objects and localized scene perturbations;(2)a physically-informed loss function that couples dynamic pose and depth predictions,designed to mitigate depth errors arising from high-speed distant objects and geometrically inconsistent motion profiles;(3)an efficient SE(3)transformation parameterization that streamlines network complexity and temporal pre-processing.Extensive experiments on the KITTI and NYU-V2 benchmarks show that our framework achieves state-of-the-art performance in both quantitative metrics and qualitative visual fidelity,significantly improving the robustness and generalization of monocular depth estimation under dynamic conditions.
基金supported by the National Natural Science Foundation of China [grant numbers 42088101 and 42375048]。
文摘Due to the lack of accurate data and complex parameterization,the prediction of groundwater depth is a chal-lenge for numerical models.Machine learning can effectively solve this issue and has been proven useful in the prediction of groundwater depth in many areas.In this study,two new models are applied to the prediction of groundwater depth in the Ningxia area,China.The two models combine the improved dung beetle optimizer(DBO)algorithm with two deep learning models:The Multi-head Attention-Convolution Neural Network-Long Short Term Memory networks(MH-CNN-LSTM)and the Multi-head Attention-Convolution Neural Network-Gated Recurrent Unit(MH-CNN-GRU).The models with DBO show better prediction performance,with larger R(correlation coefficient),RPD(residual prediction deviation),and lower RMSE(root-mean-square error).Com-pared with the models with the original DBO,the R and RPD of models with the improved DBO increase by over 1.5%,and the RMSE decreases by over 1.8%,indicating better prediction results.In addition,compared with the multiple linear regression model,a traditional statistical model,deep learning models have better prediction performance.
基金Project supported by the National Natural Science Foundation of China(52372134,12274023)the Fundamental Re search Funds for the Central Universities(FRF-EYIT-23-04)。
文摘Rare earth-doped inorganic compounds contribute mostly to the family of persistent luminescent materials due to the versatile energy levels of rare earth ions.One of the key research aims is to match the trap level stemming from the doped rare earth ion or intrinsic defects to the electronic structure of the host,and therefore thermoluminescence measurement becomes a radical technology in studying trap depth,which is one of the significant parameters that determine the properties of persistent luminescence and photostimulated luminescence.However,the results of trap depth obtained by different thermoluminescence methods are quite different so that they are not comparable.Herein,we analyzed different thermoluminescence methods,selected and improved the traditional peak position method of T_(m)/500 to be E=(-0.94Inβ+30.09)kT_(m).Only the experimental heating rate(β)is needed additionally,but the accuracy is improved greatly in most cases.This convenient and accurate method will accelerate the discovery of novel rare earth-doped materials.
基金financially supported by the POSCO-POSTECH-RIST Convergence Research Center program funded by POSCOthe National Research Foundation (NRF) grants (RS-2024-00462912, RS-2024-00416272, RS-2024-00337012, RS-2024-00408446) funded by the Ministry of Science and ICT (MSIT) of the Korean government+2 种基金the Korea Evaluation Institute of Industrial Technology (KEIT) grant (No. 1415185027/20019169, Alchemist project) funded by the Ministry of Trade, Industry and Energy (MOTIE) of the Korean governmentthe Soseon Science fellowship funded by Community Chest of Koreathe NRF PhD fellowship (RS-2023-00275565) funded by the Ministry of Education (MOE) of the Korean government。
文摘Spatial computing and augmented reality are advancing rapidly,with the goal of seamlessly blending virtual and physical worlds.However,traditional depth-sensing systems are bulky and energy-intensive,limiting their use in wearable devices.To overcome this,recent research by X.Liu et al.presents a compact binocular metalens-based depth perception system that integrates efficient edge detection through an advanced neural network.This system enables accurate,realtime depth mapping even in complex environments,enhancing potential applications in augmented reality,robotics,and autonomous systems.
基金Project(2024ZD1003704)supported by the Deep Earth Probe and Mineral Resources Exploration-National Science and Technology Major Project,ChinaProjects(51834001,52130404)supported by the National Natural Science Foundation of China。
文摘During upward horizontal stratified backfill mining,stable backfill is essential for cap and sill pillar recovery.Currently,the primary method for calculating the required strength of backfill is the generalized three-dimensional(3 D)vertical stress model,which ignores the effect of mine depth,failing to obtain the vertical stress at different positions along stope length.Therefore,this paper develops and validates an improved 3 D model solution through numerical simulation in Rhino-FLAC^(3D),and examines the stress state and stability of backfill under different conditions.The results show that the improved model can accurately calculate the vertical stress at different mine depths and positions along stope length.The error rates between the results of the improved model and numerical simulation are below 4%,indicating high reliability and applicability.The maximum vertical stress(σ_(zz,max))in backfill is positively correlated with the degree of rock-backfill closure,which is enhanced by mine depth and elastic modulus of backfill,while weakened by stope width and inclination,backfill friction angle,and elastic modulus of rock mass.Theσ_(zz,max)reaches its peak when the stope length is 150 m,whileσ_(zz,max)is insensitive to changes in rock-backfill interface parameters.In all cases,the backfill stability can be improved by reducingσ_(zz,max).The results provide theoretical guidance for the backfill strength design and the safe and efficient recovery of ore pillars in deep mining.
基金Projects(52225403,52074112)supported by the National Natural Science Foundation of ChinaProject(2022CFD009)supported by the Hubei Natural Science Foundation Innovation and Development Joint Fund Key Project,China+2 种基金Project(SDGZK2423)supported by the State Key Laboratory of Intelligent Construction and Healthy Operation and Maintenance of Deep Underground Engineering,ChinaProject(HJZKYBKT2024111)supported by the Xiangyang Federation of Social Sciences“Hanjiang Think Tank”Project,ChinaProject supported by the Hubei Superior and Distinctive Discipline Group of“New Energy Vehicle and Smart Transportation”,China。
文摘Coal seam water injection in tunnels is an effective technical measure for preventing coal mine rock bursts.This study used the improved split Hopkinson pressure bar(SHPB)to apply three equal static stresses to water-saturated coal to simulate the initial stress environment of coal at different depths.Then,dynamic mechanical experiments were conducted on the saturated coal at different depths to investigate the effects of water saturation and depth on the coal samples’dynamic mechanical properties.Under uniaxial compression and without lateral compression,the strength of coal samples decreased to varying degrees in the saturated state;under different depth conditions,the dynamic strength of coal in the saturated state decreased compared with that in the natural state.However,compared with that at 0 m,the reduction in the strength of coal under the saturated condition at 200,400,600,and 800 m was significantly reduced.The findings of this study provide a basic theoretical foundation for the prevention and control of dynamic coal mine disasters.
基金funded by the National Natural Science Foundation of China(Grant No.31801421)the Chinese Academy of Agricultural Sciences Innovation Project(Grant No.CAAS-ASTIPIVFCAAS).
文摘Eye depth is an important agronomic trait affecting tubers'appearance,quality,and processing suitability.Hence,cultivating varieties with uniform shapes and shallow eye depth are important goals for potato breeding.In this study,based on the primary mapping of the tuber eyedepth locus using a small primary-segregating population,a large secondary-segregating population with 2100 individuals was used to map the eye-depth locus further.A major quantitative trait locus for eye-depth on chromosome 10 was identified(designated qEyd10.1)using BSAseq and traditional QTL mapping methods.The qEyd10.1 could explain 55.0%of the eye depth phenotypic variation and was further narrowed to a 309.10 kb interval using recombinant analysis.To predict candidate genes,tissue sectioning and RNA-seq of the specific tuber tissues were performed.Genes encoding members of the peroxidase superfamily with likely roles in indole acetic acid regulation were considered the most promising candidates.These results will facilitate marker-assisted selection for the shallow-eye trait in potato breeding and provide a solid basis for eye-depth gene cloning and the analysis of tuber eye-depth regulatory mechanisms.
文摘AIM:To assess visual outcomes and satisfaction of a non-diffractive extended depth of focus(EDOF)intraocular lens(IOL)in individuals with ocular hypertension(OHT)and well-controlled mild glaucoma undergoing cataract surgery.METHODS:An investigator-initiated,single-center,prospective,interventional,noncomparative study conducted in Montreal,Canada.The study enrolled 31 patients(55 eyes)with OHT or mild glaucoma who received a non-diffractive EDOF IOL(Acrysof IQ Vivity).Participants underwent sequential cataract surgery with the Vivity IOL.Follow-up evaluations occurred at 1d,1,and 3mo postoperatively,assessing uncorrected distance,intermediate,and near visual acuity.Questionnaires(QUVID:Questionnaire for visual disturbances and IOLSAT:Intraocular lens satisfaction)were administered pre and post-operatively to measure visual disturbances and spectacle independence in various lighting.Safety parameters included intraocular pressure(IOP),glaucoma medications,spherical equivalence,mean deviation and pattern standard deviation or square root of lost variance on Octopus visual field.RESULTS:At 1 and 3mo postoperatively,significant improvements were observed in uncorrected distance and intermediate visual acuity.Spectacle independence was enhanced for distance and intermediate vision,especially in bright light settings.Spectacle-free intermediate vision was improved even in dim lighting.Visual disturbances,particularly glare symptoms,were reduced,and there was a notable decrease in IOP and glaucoma medication burden at 3mo.There was more hazy vision postoperatively with no impact on visual acuity and visual satisfaction.CONCLUSION:The non-diffractive EDOF lens improves distance and intermediate spectacle-free visual function in patients with OHT and well-controlled glaucoma.The findings highlight significant improvements in visual acuity,reduced glare,enhanced spectacle independence,and improved visual performance in different lighting conditions.
基金supported by the National Natural Science Foundation of China[Grant Nos.62205367 and 62141506]the Suzhou Basic Research Pilot Project[Grant Nos.SSD2023006 and SJC2021013]the National Key Research and Development Program of China[Grant No.2023YFF1205700].
文摘Maintaining the s-polarization state of laser beams is important to achieve high modulation depth in a laser-interference-based super-resolution structured illumination microscope(SR-SIM).However,the imperfect optical components can depolarize the laser beams hence degenerating the modulation depth.Here,we first presented a direct measurement method designed to estimate the modulation depth more precisely by shifting illumination patterns with equal phase steps.This measurement method greatly reduces the dependence of modulation depths on the samples,and then developed a polarization optimization method to achieve high modulation depth at all orientations by actively and quantitatively compensating for the additional phase difference using a combination of waveplate and a liquid crystal variable retarder(LCVR).Experimental results demonstrate that our method can achieve illumination patterns with modulation depth higher than 0.94 at three orientations with only one LCVR voltage,which enables isotropic resolution improvement.
文摘Object detection in occluded environments remains a core challenge in computer vision(CV),especially in domains such as autonomous driving and robotics.While Convolutional Neural Network(CNN)-based twodimensional(2D)and three-dimensional(3D)object detection methods havemade significant progress,they often fall short under severe occlusion due to depth ambiguities in 2D imagery and the high cost and deployment limitations of 3D sensors such as Light Detection and Ranging(LiDAR).This paper presents a comparative review of recent 2D and 3D detection models,focusing on their occlusion-handling capabilities and the impact of sensor modalities such as stereo vision,Time-of-Flight(ToF)cameras,and LiDAR.In this context,we introduce FuDensityNet,our multimodal occlusion-aware detection framework that combines Red-Green-Blue(RGB)images and LiDAR data to enhance detection performance.As a forward-looking direction,we propose a monocular depth-estimation extension to FuDensityNet,aimed at replacing expensive 3D sensors with a more scalable CNN-based pipeline.Although this enhancement is not experimentally evaluated in this manuscript,we describe its conceptual design and potential for future implementation.
基金supported by the National Key Research and Development Program of China(2022YFB3605902)the National Natural Science Foundation of China(52375411,52293402)。
文摘Workpiece rotational grinding is widely used in the ultra-precision machining of hard and brittle semiconductor materials,including single-crystal silicon,silicon carbide,and gallium arsenide.Surface roughness and subsurface damage depth(SDD)are crucial indicators for evaluating the surface quality of these materials after grinding.Existing prediction models lack general applicability and do not accurately account for the complex material behavior under grinding conditions.This paper introduces novel models for predicting both surface roughness and SDD in hard and brittle semiconductor materials.The surface roughness model uniquely incorporates the material’s elastic recovery properties,revealing the significant impact of these properties on prediction accuracy.The SDD model is distinguished by its analysis of the interactions between abrasive grits and the workpiece,as well as the mechanisms governing stress-induced damage evolution.The surface roughness model and SDD model both establish a stable relationship with the grit depth of cut(GDC).Additionally,we have developed an analytical relationship between the GDC and grinding process parameters.This,in turn,enables the establishment of an analytical framework for predicting surface roughness and SDD based on grinding process parameters,which cannot be achieved by previous models.The models were validated through systematic experiments on three different semiconductor materials,demonstrating excellent agreement with experimental data,with prediction errors of 6.3%for surface roughness and6.9%for SDD.Additionally,this study identifies variations in elastic recovery and material plasticity as critical factors influencing surface roughness and SDD across different materials.These findings significantly advance the accuracy of predictive models and broaden their applicability for grinding hard and brittle semiconductor materials.
基金funded by the Yangtze River Delta Science and Technology Innovation Community Joint Research Project(2023CSJGG1600)the Natural Science Foundation of Anhui Province(2208085MF173)Wuhu“ChiZhu Light”Major Science and Technology Project(2023ZD01,2023ZD03).
文摘In the dynamic scene of autonomous vehicles,the depth estimation of monocular cameras often faces the problem of inaccurate edge depth estimation.To solve this problem,we propose an unsupervised monocular depth estimation model based on edge enhancement,which is specifically aimed at the depth perception challenge in dynamic scenes.The model consists of two core networks:a deep prediction network and a motion estimation network,both of which adopt an encoder-decoder architecture.The depth prediction network is based on the U-Net structure of ResNet18,which is responsible for generating the depth map of the scene.The motion estimation network is based on the U-Net structure of Flow-Net,focusing on the motion estimation of dynamic targets.In the decoding stage of the motion estimation network,we innovatively introduce an edge-enhanced decoder,which integrates a convolutional block attention module(CBAM)in the decoding process to enhance the recognition ability of the edge features of moving objects.In addition,we also designed a strip convolution module to improve the model’s capture efficiency of discrete moving targets.To further improve the performance of the model,we propose a novel edge regularization method based on the Laplace operator,which effectively accelerates the convergence process of themodel.Experimental results on the KITTI and Cityscapes datasets show that compared with the current advanced dynamic unsupervised monocular model,the proposed model has a significant improvement in depth estimation accuracy and convergence speed.Specifically,the rootmean square error(RMSE)is reduced by 4.8%compared with the DepthMotion algorithm,while the training convergence speed is increased by 36%,which shows the superior performance of the model in the depth estimation task in dynamic scenes.
基金the Botany Graduate Program of Universidade Federal de Vicosa - PPGBot-UFV for the infrastructure and scholarshipsprovided by FAPEMIG (FORTIS/PPGBot-UFV, PPM00584-16, APQ-01309-16)+1 种基金CAPES (PROAP and Pr Int/PPGBot-UFV)CNPq (307591/2016-6, 306335/2020-4)
文摘Global change threatens mountainous plant communities,causing habitat displacement.Phylogenetic studies reveal evolutionary and ecological processes in community assembly.We examined taxonomic and phylogenetic diversity in Andean Páramos across altitudes.Our hypotheses were that increasing altitude is an environmental filter,as altitude is expected to be a stronger variable than soil depth.The Páramos,alpine vegetation in the Andes,range from 3,000 to 4,700 meters,with our plots spanning 3,200 to 4,100 meters.Sampling was conducted at six altitudinal levels,measuring soil depth,taxonomic,and phylogenetic diversity.Data analysis employed multiple linear regressions and mixed-effects models to assess the effects of soil depth and altitude.We sampled 110 angiosperm species from 70 genera,30 families,and 18 orders.Asterales and Poales were prominent.Species richness generally decreased with altitude but increased at the summit.Soil depth affected species richness and taxonomic diversity,while altitude did not.Phylogenetic diversity increased with soil depth and decreased with altitude.Phylogenetic turnover increased with altitude differences.The hypothesis that increasing altitude intensifies environmental filtering in the altitudeadapted Páramos resulting in lower species richness and more clustered phylogenetic structures,was rejected.Although species richness,Shannon diversity,and Simpson diversity decreased initially with increasing altitude,this trend was not linear because the summit presented intermediate species richness.The hypothesis that altitude is a stronger explanatory variable than soil depth was also rejected.Despite expectations,taxonomic results did not support altitude as an environmental filter,but soil depth.Greater altitude differences increased beta phylogenetic dissimilarity,supporting niche conservatism.
文摘As shale gas technology has advanced,the horizontal well fracturing model has seen widespread use,leading to substantial improvements in industrial gas output from shale gas wells.Nevertheless,a swift decline in the productivity of individual wells remains a challenge that must be addressed throughout the development process.In this study,gas wells with two different wellbore trajectory structures are considered,and the OLGA software is exploited to perform transient calculations on various tubing depth models.The results can be articulated as follows.In terms of flow patterns:for the deep well A1(upward-buckled),slug flow occurs in the Kick-off Point position and above;for the deep well B1(downward-inclined),slug flow only occurs in the horizontal section.Wells with downward-inclined horizontal sections are more prone to liquid accumulation issues.In terms of comparison to conventional wells,it is shown that deep shale gas wells have longer normal production durations and experience liquid accumulation later than conventional wells.With regard to optimal tubing placement:for well A1(upward-buckled),it is recommended to place tubing at the Kick-off Point position;for well B1(downward-inclined),it is recommended to place tubing at the lower heel of the horizontal section.Finally,in terms of production performance:well A1(upward-buckled)outperforms well B1(downward-inclined)in terms of production and fluid accumulation.In particular,the deep well A1 is 1.94 times more productive and 1.3 times longer to produce than conventional wells.Deep well B1 is 1.87 times more productive and 1.34 times longer than conventional wells.
基金supported by Japan Society for the Promotion of Science(JSPS)Grants-in-Aid for Scientific Research[Grant No.19H02992]Grant for the Environmental Research Projects,the Sumitomo Foundation[Grant No.2230116].
文摘Understanding water uptake depth and its relationship with functional traits offers valuable insights into resource-use partitioning among coexisting tree species as well as forest responses to drought.However,knowledge about water uptake patterns in vertical soil layers,especially among increasingly widespread secondary forest tree species,remains limited.In this study,we investigated interspecific and seasonal variations in water uptake depth among seven coexisting tree species over a 2-year period in a warm-temperate secondary forest in central Japan.We also analyzed the relationships of water uptake depth with tree height and functional traits,including specific leaf area(SLA),leaf dry matter content(LDMC),leaf nitrogen(N)content,and wood density(WD),to discern resource-use and-acquisition strategies.Results revealed that taller trees,especially when soil water is scarce,tend to access deeper soil water sources,indicating that water source partitioning is correlated with tree height.This interspecific and temporal variation in water sources likely stratifies trees to facilitate coexistence within the forest.Water uptake depth was primarily associated with WD and LDMC:trees absorbing more water from shallow soils during dry conditions exhibited lower WD and LDMC,indicating a proactive resource-use strategy.Conversely,SLA and leaf N content were orthogonal to water uptake depth,suggesting that strategies for acquiring belowground and aboveground resources may differ.Considering the alternation of tree species composition during secondary forest succession,our study highlights the importance of further data collection regarding root water uptake depth along successional stages to understand dynamic shifts in water uptake sources.
文摘Scour around bridge pier foundations is a complex phenomenon that can threaten structural stability.Accurate prediction of scour depth around compound piers remains challenging for bridge engineers.This study investigated the effect of foundation elevation on scour around compound piers and developed reliable scour depth prediction models for economical foundation design.Experiments were conducted under clear-water conditions using two circular piers:(1)a uniform pier(with a diameter of D)and(2)a compound pier consisting of a uniform pier resting on a circular foundation(with a foundation diameter(D_(f))of 2D)positioned at various elevations(Z)relative to the channel bed.Results showed that foundation elevation significantly affected scour depth.Foundations at or below the bed(Z/D≥0)reduced scour,while those projecting into the flow field(Z/D<0)increased scour.The optimal foundation elevation was found to be 0.1D below the bed level,yielding a 57%reduction in scour depth compared to the uniform pier due to its shielding effect against downflow and horseshoe vortices.In addition,regression,artificial neural network(ANN),and M5 model tree models were developed using experimental data from this and previous studies.The M5 model outperformed the traditional HEC-18 equation,regression,and ANN models,with a coefficient of determination greater than 0.85.Sensitivity analysis indicated that flow depth,foundation elevation,and diameter significantly influenced scour depth prediction,whereas sediment size had a lesser impact.
基金General Project of Beijing Social Science Foundation,“Research on the Internal and External Strategic Alignment of Regional Trade Agreements and the High-Quality Construction of China(Beijing)Pilot Free Trade Zone”(Project No.:21GLB021)。
文摘This paper analyzes the text of 3261 clauses of 20 RTAs signed by China,classifies them into 52 policy areas according to the international mainstream HMS method,and assigns them through coding.The clause depth of China’s RTAs is measured across three-dimensional systems(policy areas,clauses,and core clauses)and two generations of trade policy areas(WTO+,WTO-X,and all policy areas).It is observed that China’s RTAs exhibit greater depth in Industrial Products,Agricultural Products,TBT,Antidumping,Countervailing,and Investment,while showing comparatively less depth in Fiscal Policy,Innovation Policies,and related areas.
文摘The Afyon-Akşehir and Sinanpaşa grabens,located in the eastern part of the Akşehir-Simav Fault System,are important sedimentary basins in the western Anatolia,Türkiye.This region,particularly the western of Afyon-Akşehir Graben,is a significant region known for its geothermal potential.The study focuses on analyzing gravity data to identify structural elements and examine the geological structures in the basins.The edge detection and enhancement techniques such as total horizontal gradient,tilt angle of the total horizontal gradient,enhanced dip angle and curvature analysis were used to investigate the structural lineaments in the area.Furthermore,2D/3D gravity modeling techniques were utilized to investigate the sedimentary depths of the Afyon-Akşehir and Sinanpaşa grabens.Based on the findings from the edge detection studies,three distinct linear features were highlighted in addition to previously identified geological structures.3D gravity inversion modeling reveals sedimentary basin depths of up to 470 m in Sinanpaşa Graben and 720 m in the western Afyon-Akşehir Graben.As a result of the structural mapping and 2D/3D gravity modeling studies,a structural uplift that may be linked to geothermal activity was detected among the local depressions in the Afyon-Akşehir Graben.The obtained features may be of potential interest for geothermal exploration;therefore,further investigations using additional geophysical data are recommended.
